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ORGANIZER;CN="Gupta, Sumeet Kumar":mailto:guptask@purdue.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=FALSE;CN=Murat Koc
 aoglu:mailto:mkocaoglu@purdue.edu
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 y@ecn.purdue.edu:mailto:ecefaculty@ecn.purdue.edu
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ATTACH:CID:4A2661C1D4552347BBBCDF80089E7E2E@namprd22.prod.outlook.com
DESCRIPTION;LANGUAGE=en-US:Hello\n\nIt is my great pleasure to announce Pro
 f. Murat Kocaoglu's talk on March 4\, 2021 from 10AM-11AM in the Seminar S
 eries for ECE Faculty and Students. The details are below and attached. Ho
 pe to see you there.\n\nThanks\n\nSumeet\n\nZoom Link: https://purdue-edu.
 zoom.us/j/3164232249\nJoin our Cloud HD Video Meeting<https://purdue-edu.z
 oom.us/j/3164232249>\nZoom is the leader in modern enterprise video commun
 ications\, with an easy\, reliable cloud platform for video and audio conf
 erencing\, chat\, and webinars across mobile\, desktop\, and room systems.
  Zoom Rooms is the original software-based conference room solution used a
 round the world in board\, conference\, huddle\, and training rooms\, as w
 ell as executive offices and classrooms. Founded in 2011\, Zoom helps busi
 nesses and organizations bring their teams together in a frictionless envi
 ronment to get more done. Zoom is a publicly traded company headquartered 
 in San Jose\, CA.\npurdue-edu.zoom.us\n\nTitle:   Entropic Methods for Cau
 sal Discovery\n\nAbstract: Causality is a fundamental concept in multiple 
 disciplines. Causal questions arise in fields ranging from medical researc
 h to engineering\, philosophy to physics. The last few decades have witnes
 sed the development of a mathematical model of probabilistic causation by 
 Judea Pearl and many others. In this modeling framework\, directed acyclic
  graphs - called the causal graphs - arise as natural objects to capture c
 ausal relations between random variables.\n     A fundamental problem is t
 o learn the causal graph over a pair of discrete variables X\, Y from data
 . In this talk\, we first give a short summary of Pearl's framework for mo
 deling causal relations. Next\, we introduce the entropic causal inference
  framework which demonstrates that under some assumptions it is possible t
 o identify the causal graph from observational data.\n    First\, we consi
 der the setting without latent confounders and show that if the amount of 
 exogenous randomness is small\, it is possible to identify if X causes Y o
 r Y causes X. In the next setting\, we allow a latent confounder between t
 he two observed variables. We show that a similar identifiability result a
 rises if the latent confounder has small entropy. Specifically\, we show t
 hat it is possible to identify if one variable causes the other or if the 
 observed dependence is solely due to the latent confounder. In both settin
 gs\, entropic causality framework establishes connections between causal d
 iscovery and information theory. Using this connection\, we propose effici
 ent algorithms for learning the underlying causal graph from observational
  data.\n\nSpeaker Bio: Murat Kocaoglu received his B.S. degree in Electric
 al - Electronics Engineering with a minor degree in Physics from the Middl
 e East Technical University in 2010\, and M.S. degree from Koc University\
 , Turkey in 2012 under the supervision of Prof. Ozgur B. Akan and Ph.D. de
 gree from The University of Texas at Austin in 2018 under the supervision 
 of Prof. Alex Dimakis and Prof. Sriram Vishwanath. He worked as a Research
  Staff Member in the MIT-IBM Watson AI Lab in IBM Research\, Cambridge\, M
 assachusetts from 2018 to 2020. He is currently an assistant professor at 
 Purdue University in the School of Electrical and Computer Engineering. Hi
 s current research interests include causal inference\, generative adversa
 rial networks\, and information theory.\n
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SUMMARY;LANGUAGE=en-US:Webinar by Prof. Murat Kocaoglu\, Purdue: March 4 10
 AM-11AM
DTSTART;TZID=Eastern Standard Time:20210304T100000
DTEND;TZID=Eastern Standard Time:20210304T110000
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20210226T195445Z
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